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OpenObject-NAV: Open-Vocabulary Object-Oriented Navigation Based on Dynamic Carrier-Relationship Scene Graph
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OpenObject-NAV: Open-Vocabulary Object-Oriented Navigation Based on Dynamic Carrier-Relationship Scene Graph
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In everyday life, frequently used objects like cups often have unfixed positions and multiple instances within the same category, and their carriers frequently change as well. As a result, it becomes challenging for a robot to efficiently navigate to a specific instance. To tackle this challenge, the robot must capture and update scene changes and plans continuously. However, current object navigation approaches primarily focus on semantic-level and lack the ability to dynamically update scene representation. This paper captures the relationships between frequently used objects and their static carriers. It constructs an open-vocabulary Carrier-Relationship Scene Graph (CRSG) and updates the carrying status during robot navigation to reflect the dynamic changes of the scene. Based on the CRSG, we further propose an instance navigation strategy that models the navigation process as a Markov Decision Process. At each step, decisions are informed by Large Language Model's commonsense knowledge and visual-language feature similarity. We designed a series of long-sequence navigation tasks for frequently used everyday items in the Habitat simulator. The results demonstrate that by updating the CRSG, the robot can efficiently navigate to moved targets. Additionally, we deployed our algorithm on a real robot and validated its practical effectiveness.
Forward citations
Cited by 3 Pith papers
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Semantic Evidence Regulation via Relational Bias for Zero-Shot Object Navigation
DB-Nav/SER-Nav improves zero-shot object navigation by reranking frontier goals using activation from object co-occurrence and inhibition from similar distractors and failed visits.
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Semantic Evidence Regulation via Relational Bias for Zero-Shot Object Navigation
DB-Nav improves object navigation by factorizing target relations into activation and inhibition biases within a relational exploration graph, yielding higher success rates and SPL on ObjectNav benchmarks.
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MORN: Metacognitive Object-Goal Regulation for Resource-Rational Long-Horizon Navigation
MORN augments frozen VLM-based object navigation agents with a System 2 meta-controller using Potentiality Index, Persistence Gating, and Evidence Accumulation to improve goal completion rate from 0.23 to 0.30 and red...
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